Aspects of the present invention relate to mobile robots, and more particularly to the mapping of environments in which mobile robots operate, to facilitate movement of mobile robots within those environments.
As a system that enables a mobile robot to map its environment and maintain working data of its position within that map, simultaneous localization and mapping (SLAM) is both accurate and versatile. Its reliability and suitability for a variety of applications make it a useful element for imparting a robot with some level of autonomy.
Typically, however, SLAM techniques tend to be computationally intensive and thus their efficient execution often requires a level of processing power and memory capacity that may not be cost effective for some consumer product applications.
For those facing the low-cost production targets necessary for competition in the consumer market, it is unlikely that an economic hardware environment would include processing and memory capacities capable of supporting adequately a robust SLAM system. It therefore is imperative that developers seek ways to facilitate efficient execution of the core SLAM algorithms within the limits of the computational capacities they have. Generally, such optimization schemes would seek to use processing power and system bandwidth judiciously, which might mean simplifying some of the SLAM algorithms in ways that do not critically compromise their performance, or reducing input data size or bandwidth.